Interaction between Age, Sex, and Mental Health Status as Precipitating Factors for Symptom Presentation in Concussed Individuals
Bibliographic record
Abstract
Concussions are among the most common neurological conditions, with emergency departments and sports injury clinics seeing hundreds of patients each year. The consideration of risk factors such as age, sex, and comorbid conditions are very important when looking at individual physiological and psychological outcomes after a concussion. The purpose of this study was to look at four comorbid conditions (depression, anxiety, behavioural disorder, or learning disability) and identify any interactions with age and sex in symptom presentation after suffering a concussion. A total of 4,865 participants from the CCMI (Complete Concussion Management Inc.) dataset were used with 1,577 self-identified with a diagnosis of anxiety, depression, a behavioural disorder, or a learning disability. Fixed-factor analyses of variance were used with age and sex as fixed, grouping factors and symptom total and severity as dependent measures. For the individuals who did not have one of the 4 mental health conditions (3,288 control participants), symptom total and symptom severity increased with age ( p<0.05 ), and females showed more symptoms and a higher symptom severity than males across all ages ( p<0.05 ). A diagnosis of anxiety or depression exacerbated total symptoms and symptom severity from 25–50% above control levels in the 19 and under age groups, while depression or anxiety exacerbated total symptoms and severity by 10–15% in males more than females over 20. A diagnosis of a behavioural disorder or a learning disability exacerbated symptom severity by approximately 50% above control levels in 13–19–year-old females and in males of 30 years and older. This study highlights how the presence of a mental health condition may alter concussion symptom presentation dependent on age and sex. The identification of risk factors and how they may interact can be of great value to health care providers who manage concussion symptoms and recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".